mlops data engineers

MLOps for Data Scientists

Meet the only model governance solution you will need.

The Advantage of MLOps for Data Scientists

With numerous processes and teams involved in getting models into production, many data scientists find that their models get stuck at the finish line.

Enter MLOps, a solution that provides data scientists with an easier, more efficient way to deploy, maintain, monitor, and update models. Start getting models into production and bridging the gap between stakeholder teams so that you can focus on data science.
MLOps outlined
Deployment
MLOps offers deployment that is totally agnostic. You pick which platform you want to deploy on. You pick which frameworks or languages you want to use.
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Monitoring
Monitoring models is essential to ensuring that they are continually producing value. MLOps gives you a system for monitoring all your models, no matter where they are deployed or what frameworks you used to build the models.
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Production Lifecycle Management
Your models will need to be updated. Manual updates are time-consuming and problematic. Lifecycle management makes it easier for data scientists to manage a large portfolio of production models.
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Production Model Governance
Deployment is just the start. It’s also important to have in place robust governance practices, review processes, and tools to minimize risk and ensure regulatory compliance.

See What MLOps Can Do for Data Scientists

DataRobot MLOps allows data science leaders and teams to embed cutting edge predictive models in an efficient and value-driven way no matter what. From agents to being cloud agnostic, MLOps is flexible.

Three Key Feature Sets

Serving Predictions

Unleash the ability to work with different types and shapes of data that serve your needs.

  • Real-time predictions
  • Batch predictions
  • Service health monitoring
  • Time series predictions
  • Image and geospatial data types
  • Java scoring code
  • Portable docker image

Operating at Scale

Use and build upon the foundation you already have.

  • Monitoring diverse prediction environments
  • Alerts
  • Audit logs
  • Versioning and lineage
  • Change approval workflows
  • No-code prediction GUI
  • Value and use case tracking
  • RBAC
  • Repo integration

Making ML Trustworthy

Deploy reliable, trustworthy, and unbiased models.

  • Data drift analysis
  • Accuracy analysis
  • Anomaly warnings
  • Prediction explanations
  • Champion/Challenger gates into production
  • Humble AI – built in mechanisms ensuring trust in your models
  • Prediction intervals
Agent based

Agents

The Only Scalable MLOps Architecture

Monitoring agents can get you to the scale of putting thousands and hundreds of thousands of models into production. Regardless of where your model is built — cloud, Spark, Azure, servers — you will be able to access your models from one central hub. Use what you have today and manage in one view.

MLOps Customers

Companies across every industry leverage DataRobot’s MLOps solution, such as:
PNC
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fnb
EmpiricHealthLogo Line
Orix
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scout24
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lendico
csinsurance
  • I really think using DataRobot MLOps is the reason why we didn’t have to stress about it [COVID] as much as other companies have. The only reason we were comfortable in doing that is that when we see performance changes via MLOps we can throw everything automatically back into DataRobot AutoML and see what it tells us in terms of model comparison and see what we need to do based on where we’re at at that point of time.
    Clayton Howard

    Director of Analytics, Net Pay Advance

  • DataRobot not only helped us to reduce overhiring by 60%, but we were even able to increase sales by an unknown amount by rectifying underhiring, fulfilling more orders in our fulfillment centers.

    – Customer, Manager of Data Science, Experimentation, and Research, eCommerce, Retail

  • DataRobot has helped our data science team to drastically accelerate our work. What would previously have taken us two-and-a-half weeks can now be done in hours. It’s like my group of 10 is really a group of 25, which would add substantially more costs for the same value.

    – Customer, Head of Data Science, Healthcare

  • The 10% increase in SKUs has had a substantial effect, and we plan to further optimize our supply chain and inventory management, resulting in savings of up to $200 million.

    – Customer, Vice president of Advanced Analytics and Data Engineering, Manufacturing

    Take the next step to managing and governing your AI.